Minh Tuan Ho, Minh-Son Dang, Phat-Dat Truong et al. · Results in Engineering · 2026
• Innovative CFD-ML framework optimizes screw geometry for food extrusion. • Random Forest surrogate accurately predicts complex process responses. • NSGA-II effectively balances axial force and specific mechanical energy. • Sensitivity analysis identifies clearance as the primary design driver. • Data-driven design approach supports efficient localized food production. This study presents an integrated computational framework for optimizing screw geometry in a compact food extruder by coupling computational fluid dynamics (CFD) simulations with machine learning (ML) and multi-objective optimization. Three-dimensional CFD simulations are performed using COMSOL Multiphysics to capture the complex thermo-fluid behavior within the extruder and to quantify key process responses. A Design of Experiments (DOE) based on Latin Hypercube Sampling (LHS) is employed to efficiently explore the high-dimensional design space of screw geometric parameters. Based on the CFD-generated dataset, a Random Forest (RF) regression model is developed as a surrogate to approximate the nonlinear relationships between screw geometry and performance indicators, achieving average relative prediction errors of approximately 8% for the axial surface force ( F S x ) and 5% for the specific mechanical energy ( SME ). Multi-objective optimization is subsequently conducted using three evolutionary algorithms, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Non-dominated Sorting Genetic Algorithm III (NSGA-III), and the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), to identify optimal trade-offs between minimizing F S x and SME . The resulting Pareto fronts obtained from the three algorithms are systematically compared to evaluate their convergence and solution diversity. The Pareto-optimal solutions highlight the competing effects of screw geometry on process performance and provide quantitative guidance for screw design improvement. Overall, the proposed CFD-driven, surrogate-assisted multi-objective optimization framework shows strong potential for smart manufacturing and intelligent design of advanced food extrusion systems.
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Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
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Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
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Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
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Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
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Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
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Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
Cited by 0Open access
Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
Cited by 0Open access
Yesong Wang (331723), Zixuan Sun (3495020), Jisheng Liu (219678) et al. · Figshare · 2024
<div><p>To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.</p></div>
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Leila S. Mamulashvili, Lali T. Osadze, Maka Sosanidze · Business Inform · 2025
Nowadays, timely transformation of information is important for the viability of an organization. Big data solutions directly affect how an organization should work with the help of artificial intelligence components. The article shows an algorithmic approach to strategic planning and performance assessment of e-business. Various artificial intelligence methodologies and their use in various applications of large organizations are shown. The conception of genetic algorithms is presented, which is related to e-business strategy in various applications. Genetic algorithms can be used to solve e-business problems, especially for strategic planning and performance evaluation, leading to improved overall performance of large organizations. A new scheme for e-business strategy planning and performance evaluation, based on adaptive algorithmic modeling techniques, is used to improve the performance of genetic algorithms. The proposed algorithmic approach can be effectively used to solve a wide class of e-business and strategic management problems. In the context of “Genetic Algorithm Optimization, Genetic Algorithm Optimization of Business Strategies”, we can delve into the future trends of genetic algorithms optimization for business strategies: 1) The development and improvement of genetic algorithms is expected to lead to improved performance in business strategy optimization. This can be achieved through more efficient selection mechanisms, crossover techniques, and mutation operators; 2) The integration of genetic algorithms with machine learning techniques holds great potential for business strategy optimization. By combining the power of genetic algorithms with the ability to learn from data, businesses can uncover hidden patterns and make more informed decisions; 3) Genetic algorithms are well suited to solving multi-objective optimization problems, where multiple conflicting objectives must be considered simultaneously. Future trends may focus on developing advanced techniques to effectively handle such complex scenarios; 4) As technology advances, genetic algorithms can be used in real-time scenarios, allowing businesses to optimize their strategies on the fly. This can be especially useful in dynamic environments where rapid adaptation is crucial; 5) Genetic algorithms can be combined with other optimization techniques, such as simulated annealing or particle swarm optimization, to create hybrid approaches. These hybrid methods can leverage the strengths of different algorithms and provide more robust optimization solutions.
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Nikolas Hohmann, Mariusz Bujny, Jürgen Adamy et al. · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
In the context of real-world path planning applications for Unmanned Aerial Vehicles (UAVs), aspects such as handling of multiple objectives (e.g., minimizing risk, path length, travel time, energy consumption, or noise pollution), generation of smooth trajectories in 3D space, and the ability to deal with urban environments have to be taken into account jointly by an optimization algorithm to provide practically feasible solutions. Since the currently available methods do not allow for that, in this paper, we propose a holistic approach for solving a Multi-Objective Path Planning (MOPP) problem for UAVs in a three-dimensional, large-scale urban environment. For the tackled optimization problem, we propose an energy model and a noise model for a UAV, following a smooth 3D path. We utilize a path representation based on 3D Non-Uniform Rational B-Splines (NURBS). As optimizers, we use a conventional version of an Evolution Strategy (ES), two standard Multi-Objective Evolutionary Algorithms (MOEAs) - NSGA2 and MO-CMA-ES, and a gradient-based L-BFGS-B approach. To guide the optimization, we propose hybrid versions of the mentioned algorithms by applying an advanced initialization scheme that is based on the exact bidirectional Dijkstra algorithm. We compare the different algorithms with and without hybrid initialization in a statistical analysis, which considers the number of function evaluations and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 3D urban path planning scenario in New York City, based on real-world data exported from OpenStreetMap. The examination's results indicate that hybrid initialization is the main factor for the efficient identification of near-optimal solutions.
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Tianzhe Zhang, Xu Chen, Michail E. Kiziroglou et al. · 2025
Millimeter-scale delta robots for micro-medical applications leverage their unique parallel structure, enabling high speed, acceleration, and tracking accuracy. However, achieving precise and stable control for these robots under haptic and external disturbances remains challenging. To address these challenges, an onboard visual method has been recently introduced to enhance system feedback accuracy. Based on this, an advanced model-free adaptive control (MFAC) approach is proposed in this paper, operating independently of an explicit mathematical model and relying solely on system input and output data. The MFAC framework is further augmented with predictive control and multi-objective parameter optimization algorithms, enhancing its capability to achieve accurate and stable motion control.
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Shuguang Xiong, Huitao Zhang, Meng Wang et al. · Optimizations in Applied Machine Learning · 2024
Motor design involves a variety of complex parameters, and traditional approaches often rely on experience and experimentation, which can be inefficient and challenging to optimize. With the rapid growth of industries such as electric vehicles and intelligent manufacturing, the demand for improved motor performance continues to rise. Optimizing motor designs under multi-objective and multi-constraint conditions has become a critical challenge. To address this, this paper introduces a motor design performance optimization algorithm that leverages Graph Neural Networks (GNN) and adaptive weighting techniques. GNN, a deep learning model adept at handling complex structured data, is capable of modeling the relationships between multiple parameters in motor design. Its feature propagation mechanism allows for automatic extraction of essential features, effectively addressing the limitations of traditional methods in capturing parameter dependencies. Additionally, Mixed-Integer Linear Programming (MILP) serves as a robust global optimization tool, capable of finding the optimal solution even in the presence of complex decision variables and constraints, overcoming the global convergence issues associated with conventional optimization algorithms. The adaptive weighting mechanism further enhances the algorithm by dynamically adjusting the weights based on the parameters' influence on motor performance, ensuring more accurate and adaptable optimization results across different scenarios. By combining these three techniques, this paper aims to resolve issues related to inefficiency, poor global convergence, and the static nature of parameter weighting in traditional motor design optimization. This approach integrates advanced machine learning models and optimization algorithms to create an efficient framework for motor design performance optimization.
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Shu‐Chuan Chen, Hsien-Ming Chen, Han-Kwang Chen et al. · Processes · 2024
The shift from Industry 4.0 to Industry 5.0 represents a significant evolution toward sustainable, human-centric manufacturing. This paper explores how advanced multi-objective optimization techniques can integrate Artificial Intelligence (AI) with human insights to enhance both sustainability and customization in manufacturing. We investigate specific optimization methods, including genetic algorithms (GAs), Particle Swarm Optimization (PSO), and reinforcement learning (RL), which are tailored to balance efficiency, waste reduction, and carbon footprint. Our proposed framework enables human creativity to interact with AI-driven processes, embedding human input into a computational structure that adapts dynamically to operational goals. By linking optimization directly to environmental impacts, such as reducing waste, energy consumption, and carbon emissions, this study establishes a pathway toward environmentally sustainable production. This research fills existing gaps by offering a detailed, practical model that harmonizes theoretical insights with applications in personalized manufacturing environments. In this regard, it contributes to the ongoing development of Industry 5.0, emphasizing how AI and human collaboration can foster intelligent, adaptable, and sustainable manufacturing systems.
Cited by 36Open access
Chaitanya Udatha, G. Lakshmeeswari · International Journal of Engineering Trends and Technology · 2023
With cutting-edge services available via subscription, the usage of cloud technology is rapidly increasing in daily life using advanced services. Efficient management of services and delivering them on demand require proper scheduling of resources and requests. Load balancing aware scheduling techniques are employed to accomplish this, which distribute requests uniformly among resources and optimize resource utilization. In cloud computing, load balancers are essential for balancing workloads on resources. They ensure even workload distribution across all resources by transferring workloads from overloaded to underloaded resources. The proposed Load Balancing Improved Multi-Objective Particle Swarm Optimization (LBIMOPSO) technique aims to manage load uniformly and allocate tasks to the best-suited virtual machines. It is a robust optimization technique that considers multiple objective functions simultaneously and effectively balances workloads in a cloud computing environment. However, according to an existing survey, there is an improvement in makespan performance compared to proper load balancing across virtual machines. Therefore, the proposed LBIMOPSO algorithm improves resource utilization, makespan, and load balance deviation compared to traditional swarm-intelligence-based ant colony and particle swarm optimization algorithms.
Cited by 2Open access
Liu, Ao, Avrakhov, Pavel, Jing, Chunguang et al. · JACOW · 2019
In order to achieve sharp, high resolution real-time imaging, electrons in a MeV UEM (ultrafast electron microscope) beamline need to minimize instabilities. The Superconducting RF (SRF) photocathode gun is a promising candidate to produce highly stable electrons for UEM/UED applications. It operates in an ultrahigh Q, CW mode, and dissipates a few watts of RF power, which make it possible to achieve a 10s ppm level of beam stability by using modern RF control techniques. In order to find the best performance of the gun design, an optimization procedure is required. pyaopt is a Python-based optimization suite that supports multi-objective optimizations using advanced algorithms. In this paper, the novel SRF photogun design and its optimizations through pyaopt and Astra’s beam simulations will be discussed.
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Xinran Chen, Xiaoxue Feng, Xinyi Jiang et al. · Expert Systems with Applications · 2025
The electromagnetic spectrum serves as a critical resource in advanced fields such as wireless communications, radar detection, and electronic warfare. The Frequency Assignment Problem (FAP) represents a complex optimization issue, encompassing the rational allocation of limited frequency resources to minimize electromagnetic interference (EMI), characterized by multiple objectives, intricate constraints, and dynamic characteristics. Traditionally, FAP is often perceived to a static dimension problem with one-to-one correspondence between equipment and frequencies. However, introducing flexible frequency handoff in the time-domain, it evolves into a one-to-many uncertain relationship, resulting in dynamic variable dimensions of FAP. To address the challenges, this paper create a cross-swarm dimension mutation strategy to generate heterogeneous multi-swarms with diverse variable structures. The life-cycle is set for mutated swarms to implement elimination mechanism, actualizing dynamic adjustment for quantity of swarms. A Dynamic Heterogeneous Multi-Swarm PSO (DHMSPSO) is proposed, which provides a new perspective for solving optimization problems with dynamic variable dimension, and be applied to develop multi-objective frequency assignment algorithm. Ultimately, through simulation experiments on FAP, comparative analysis and statistical tests between DHMSPSO and other optimization algorithms validate the feasibility and superiority of DHMSPSO. And futher simulation for elimination mechanism demonstrates that can prevent excessive swarm expansion and improve the optimization efficiency.
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Prakas Gopal , Samy · University of Malaya Students Repository · 2024
This research undertakes a comprehensive exploration aimed at optimizing cancer therapy by integrating mathematical modelling and advanced optimization methodologies. The central focus revolves around the dual objective of minimizing tumour cell populations while optimizing chemotherapy administration to ensure healthy effector-immune cell levels. Anchored in mathematical modelling using ordinary differential equations (ODE), the study culminates in a Multi-Objective Optimal Control Problem (MOCP), categorizing into Pure MOCP (P-MOCP) and Hybrid MOCP (H-MOCP). The realization of this objective is steered by two distinct methodologies: the Method for Pure Multi-Objective Optimal Control (PMM) and the Hybrid Method (HM). Leveraging state-of-the-art Multi-Objective Optimization algorithms, the research delves into the intricate dynamics of tumour and effector cell interactions within the context of chemotherapy. The study harnesses MATLAB's ode45 solver, tailored for solving ODE. Utilizing robust Runge-Kutta methods, the solver adeptly navigates the complexities of the ODE model, particularly useful when analytical solutions are challenging to obtain. Advancing multi-objective optimization techniques for cancer treatment strategies, the study strategically incorporates Swarm Intelligence (SI) and Evolutionary Algorithms (EA). Diverging in constraints handling and Pontryagin Maximum Principle (PMP) application, the PMM and HM methodologies are evaluated through the lens of SI and EA, and their outcomes are visualized via the Pareto Optimal Front (PF). Over the simulation period, consistent trends of tumour population reduction and optimized drug administration reaffirm the methodologies' inherent effectiveness. The evaluation process employs the Hypervolume (HV) indicator and Inverted Generational Distance (IGD) to scrutinize solution efficacy and coverage. The HM emerges as a dominant strategy, driven by the Multi-Objective Differential Evolution (MODE) algorithm under literature-based control parameter settings for the mathematical model. This methodology efficiently positions solutions on the PF with minimal distances from the origin, signifying optimality. To comprehend the intricate dynamics of cancer and effector cells under chemotherapy's influence, the study undertakes equilibrium point stability analysis through the Jacobian matrix and eigenvalues. Stability and bifurcation analyses illuminate the model's responsiveness to varying control parameters. Empirical validation and performance assessment are conducted through rigorous benchmarking simulations on the PlatEMO platform, covering ten algorithms. The Multi-Objective Particle Swarm Optimizer (MOPSO) demonstrates superior performance in the HM, while the Modified Multi-Objective Particle Swarm Optimizer (M-MOPSO) excels within the PMM, highlighting its crucial role in optimizing cancer therapy with enhanced control parameters. The conclusion drawn from these analyses, spanning key metrics such as runtime, IGD, and HV, and utilizing the methodologies employed in this study, establishes the superior performance of HM in the first phase, whereas in the second phase, PMM emerges as the more effective approach. This finding underscores the HM's and PMM’s potential to revolutionize chemotherapy strategies, offering a promising avenue for advancing cancer therapy while mitigating adverse effects on healthy cell populations. Beyond demonstrating transformative potential, the research accentuates innovative methodologies' ability to shape the future of cancer treatment, instilling optimism in patients and healthcare professionals alike.
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Xing Fu, Khairul Kabir, Xiaorui Wang · 2013
Abstract — Multi-core processors are anticipated to become a major development platform for real-time systems. However, existing power management algorithms are not designed to sufficiently utilize the features available in many multi-core processors, such as shared L2 caches and per-core DVFS, to effectively minimize processor energy consumption while providing real-time guarantees. In this paper, we propose a two-level utilization control solution for energy efficiency in multi-core real-time systems. At the core level, our solution addresses two optimization objectives: controlling the CPU utilization of each core to its desired schedulable bound and minimizing the core energy consumption by adopting per-core DVFS and dynamic L2 cache partitioning to adapt both the CPU frequency-dependent and independent portions of the task execution times of the core. Since traditional control theory cannot handle multiple optimization objectives, a novel utilization controller is designed based on advanced multi-objective model predictive control theory. At the processor level, a cache demand arbitrator is proposed to coordinate the cache size demand from each core and conduct dynamic cache resizing to minimize the leakage power consumption of the shared L2 caches. Extensive experiments with well-known benchmarks demonstrate that our solution outperforms two state-of-the-art power management algorithms that do not consider L2 caches or per-core DVFS, by having more accurate utilization control and less power consumption. I.
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Pranav Tripathi · Rutgers University Community Repository (Rutgers University) · 2026
This study presents a comprehensive computational analysis and optimization of the chemical vapor deposition (CVD) process for silicon thin films in various reactor geometries, including horizontal, angled, and vertical impinging chambers. Using ANSYS Fluent, the flow dynamics, heat transfer, and chemical kinetics of silane decomposition were modeled, with the simulations validated against experimental and numerical data from the literature. The results demonstrate that reactor geometry significantly influences deposition rate and film uniformity, with the vertical impinging configuration providing the highest uniformity and stable deposition, while angled chambers achieve higher deposition rates but at the cost of coverage consistency. A detailed parametric study investigated the effects of temperature, flow velocity, precursor concentration, chamber height, and deposition angle on performance, revealing complex trade-offs between deposition rate and acceptable area. Multi-objective optimization using response surface methodology and genetic algorithms was performed to generate Pareto fronts, enabling identification of optimal operating conditions for each reactor type. The analysis highlights the importance of balancing process parameters to achieve both high deposition efficiency and uniform film coverage. Future work will focus on expanding model validation across additional geometries, incorporating advanced chemical kinetics and multiscale phenomena, exploring rotating susceptor components, and implementing robust multi-objective optimization strategies to further enhance CVD performance.
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